Data center management using digital twins

ABSTRACT

A method, apparatus, system, and computer program product for managing a network data processing system. Digital twins of physical devices in the network data processing system are run by a computer system. The digital twins process workloads and the digital twins communicate with each other. An impact on a number of parameters for a first set of the digital twins that a second set of the digital twins has on the first set of the digital twins is identified by the computer system. A set of actions is performed by the computer system based on the impact on the number of parameters.

BACKGROUND 1. Field

The disclosure relates generally to an improved computer system and,more specifically, to a method, apparatus, computer system, and computerprogram product to manage a group of physical devices in a network dataprocessing system.

2. Description of the Related Art

A data center is a group of server computers that are in communicationwith each other over a network. The group of server computers can belocated in a building or in multiple buildings that house the servercomputers and other components that support the server computers. Theseother components can include, for example, racks, storage systems, powerdevices, cooling devices, communications equipment, and other systems ordevices.

A data center can be used by users and organizations to store data. Datacenters can also provide processing resources for various requests fromusers. For example, a client may run a database on servers in a datacenter. The users may also provide access to word processing, aspreadsheet, an email, and other applications through the data center.For example, the data center can provide a cloud computing environmentto users.

A data center can contain thousands of server racks in which powerusages of over 400 MWs can occur. This power usage can result in anundesired increase in temperature in the data center. Temperatureincreases can affect server performance, the server life, andmaintenance needs. Environmental control systems are employed in datacenters to provide airflow and cooling to maintain a desired temperaturefor running servers.

SUMMARY

According to one embodiment of the present invention, a method manages anetwork data processing system. Digital twins of physical devices in thenetwork data processing system are run by a computer system. The digitaltwins process workloads and the digital twins communicate with eachother. An impact on a number of parameters for a first set of thedigital twins that a second set of the digital twins has on the firstset of the digital twins is identified by the computer system. A set ofactions is performed by the computer system based on the impact on thenumber of parameters.

According to another embodiment of the present invention, a networkmanagement system comprises a computer system and a hardware manager inthe computer system. The hardware manager runs digital twins of physicaldevices in a network data processing system. The digital twins processworkloads and the digital twins communicate with each other. Thehardware manager identifies an impact on a number of parameters that afirst set of the digital twins has on a second set of the digital twins.The hardware manager performs a set of actions based on the impact onthe number of parameters.

According to yet another embodiment of the present invention, a computerprogram product manages a network data processing system. The computerprogram product comprises a computer-readable storage media with firstprogram code, second program code, and third program code stored on thecomputer-readable storage media. The first program code is executable bya computer system to cause the computer system to run digital twins ofphysical devices in the network data processing system. The digitaltwins process workloads and the digital twins communicate with eachother. The second program code is executable by the computer system tocause the computer system to identify an impact on a number ofparameters that a first set of the digital twins has on a second set ofthe digital twins. The third program code is executable by the computersystem to cause the computer system to perform a set of actions based onthe impact on the number of parameters.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a pictorial representation of a network of data processingsystems in which illustrative embodiments may be implemented;

FIG. 2 is a set of functional abstraction layers provided by the cloudcomputing environment in FIG. 1 in accordance with an illustrativeembodiment;

FIG. 3 is a pictorial representation of a network of data processingsystems in which illustrative embodiments may be implemented;

FIG. 4 is a block diagram of a network management environment inaccordance with an illustrative embodiment;

FIG. 5 is a block diagram of a network manager in accordance with anillustrative embodiment;

FIG. 6 is an illustration of digital twin information in accordance withan illustrative embodiment;

FIG. 7 is an illustration of network information in accordance with anillustrative embodiment;

FIG. 8 is a flowchart of a process for managing a network dataprocessing system in accordance with an illustrative embodiment;

FIG. 9 is another flowchart of a process for managing a network dataprocessing system in accordance with an illustrative embodiment;

FIG. 10 is yet another flowchart of a process for managing a networkdata processing system in accordance with an illustrative embodiment;

FIG. 11 is a flowchart of a process identifying an impact on a number ofparameters in accordance with an illustrative embodiment; and

FIG. 12 is a block diagram of a data processing system in accordancewith an illustrative embodiment.

DETAILED DESCRIPTION

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer-readable storagemedium (or media) having computer-readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer-readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer-readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer-readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer-readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer-readable program instructions described herein can bedownloaded to respective computing/processing devices from acomputer-readable storage medium or to an external computer or externalstorage device via a network, for example, the Internet, a local areanetwork, a wide area network and/or a wireless network. The network maycomprise copper transmission cables, optical transmission fibers,wireless transmission, routers, firewalls, switches, gateway computers,and/or edge servers. A network adapter card or network interface in eachcomputing/processing device receives computer-readable programinstructions from the network and forwards the computer-readable programinstructions for storage in a computer-readable storage medium withinthe respective computing/processing device.

Computer-readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer-readable program instructions may run entirelyon the user's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer, or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider). In some embodiments, electronic circuitry including, forexample, programmable logic circuitry, field-programmable gate arrays(FPGA), or programmable logic arrays (PLA) may run the computer-readableprogram instructions by utilizing state information of thecomputer-readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer-readable program instructions.

These computer-readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which are run via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. Thesecomputer-readable program instructions may also be stored in acomputer-readable storage medium that can direct a computer, aprogrammable data processing apparatus, and/or other devices to functionin a particular manner, such that the computer-readable storage mediumhaving instructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer-readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which run on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, run concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be processed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

The illustrative embodiments recognize and take into account a number ofdifferent considerations. For example, the illustrative embodimentsrecognize and take into account that different locations in a datacenter may have higher amounts of heat generation as compared to otherlocations. The illustrative embodiments recognize and take into accountthat the location of workloads on servers can affect the amount of heatgenerated in a particular location in a data center. The illustrativeembodiments recognize and take into account that hardware manufacturersare generally unaware of how their equipment impacts performance andbehavior of the hardware devices supplied by the hardware manufacturers.

Therefore, it would be desirable to have a method and apparatus thattake into account at least some of the issues discussed above, as wellas other possible issues. For example, it would be desirable to have amethod and apparatus that overcome a technical problem with determiningan impact of the operation of physical devices on each other within anetwork such as server computers and other physical devices in a datacenter. For example, the illustrative embodiments recognize and takeinto account that it would be desirable to take into account the thermaleffects of workloads running on different server computers in differentlocations in a data center. Further, the illustrative embodiments alsorecognize and take into account that workloads processed by servercomputers can have other impacts on processor use, memory use, or otherparameters.

Thus, the illustrative embodiments provide a method, apparatus, system,and computer program product for managing a network. In one illustrativeexample, digital twins of physical devices in a network data processingsystem are run by a computer system. The digital twins process workloadsand the digital twins communicate with each other. An impact on a numberof parameters for a first set of the digital twins that a second set ofthe digital twins has on the first set of the digital twins isidentified by the computer system. A set of actions is performed by thecomputer system based on the impact on the number of parameters.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 1, an illustration of cloud computing environment50 is depicted. As shown, cloud computing environment 50 includes one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Cloud computingnodes 10 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 50 tooffer infrastructure, platforms, and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1 are intended to be illustrative only and that cloud computingnodes 10 in cloud computing environment 50 can communicate with any typeof computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 1) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 2 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

As used herein, a “set of,” when used with reference to items, means oneor more items. For example, a “set of functional abstraction layers” isone or more functional abstraction layers.

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture-based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and network management 96. Network management96 provides a service for managing a network in cloud computingenvironment 50 in FIG. 1 or a network in a physical location thataccesses cloud computing environment 50 in FIG. 1. In the illustrativeexample, this network can be, for example, one of a data center, amanufacturing facility, or some other type of location.

With reference now to FIG. 3, a pictorial representation of a network ofdata processing systems is depicted in which illustrative embodimentsmay be implemented. Network data processing system 300 is a network ofcomputers in which the illustrative embodiments may be implemented.Network data processing system 300 contains network 302, which is themedium used to provide communications links between various devices andcomputers connected together within network data processing system 300.Network 302 may include connections, such as wire, wirelesscommunication links, or fiber optic cables.

In the depicted example, server computer 304 and server computer 306connect to network 302 along with storage unit 308. In addition, clientdevices 310 connect to network 302. As depicted, client devices 310include client computer 312, client computer 314, and client computer316. Client devices 310 can be, for example, computers, workstations, ornetwork computers. In the depicted example, server computer 304 providesinformation, such as boot files, operating system images, andapplications to client devices 310. Further, client devices 310 can alsoinclude other types of client devices such as mobile phone 318, tabletcomputer 320, and smart glasses 322. In this illustrative example,server computer 304, server computer 306, storage unit 308, and clientdevices 310 are network devices that connect to network 302 in whichnetwork 302 is the communications media for these network devices. Someor all of client devices 310 may form an Internet-of-things (IoT) inwhich these physical devices can connect to network 302 and exchangeinformation with each other over network 302.

Client devices 310 are clients to server computer 304 in this example.Network data processing system 300 may include additional servercomputers, client computers, and other devices not shown. Client devices310 connect to network 302 utilizing at least one of wired, opticalfiber, or wireless connections.

Program code located in network data processing system 300 can be storedon a computer-recordable storage media and downloaded to a dataprocessing system or other device for use. For example, program code canbe stored on a computer-recordable storage media on server computer 304and downloaded to client devices 310 over network 302 for use on clientdevices 310.

In the depicted example, network data processing system 300 is theInternet with network 302 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers consisting of thousands of commercial, governmental,educational, and other computer systems that route data and messages. Ofcourse, network data processing system 300 also may be implemented usinga number of different types of networks. For example, network 302 can becomprised of at least one of the Internet, an intranet, a local areanetwork (LAN), a metropolitan area network (MAN), or a wide area network(WAN). FIG. 3 is intended as an example, and not as an architecturallimitation for the different illustrative embodiments.

As used herein, a “number of,” when used with reference to items, meansone or more items. For example, a “number of different types ofnetworks” is one or more different types of networks.

Further, the phrase “at least one of,” when used with a list of items,means different combinations of one or more of the listed items can beused, and only one of each item in the list may be needed. In otherwords, “at least one of” means any combination of items and number ofitems may be used from the list, but not all of the items in the listare required. The item can be a particular object, a thing, or acategory.

For example, without limitation, “at least one of item A, item B, oritem C” may include item A, item A and item B, or item B. This examplealso may include item A, item B, and item C or item B and item C. Ofcourse, any combinations of these items can be present. In someillustrative examples, “at least one of” can be, for example, withoutlimitation, two of item A; one of item B; and ten of item C; four ofitem B and seven of item C; or other suitable combinations.

In this illustrative example, data center 330 is connected to network302. As depicted, data center 330 can provide at least one of storage orprocessing resources to client devices such as client devices 310. Forexample, data center 330 can provide storage for documents, images,spreadsheets, or other files generated by client devices 310. Further,data center 330 can provide processing resources such as word processingapplications, spreadsheet applications, database applications, or othertypes of applications that client devices 310 can utilize.

In this illustrative example, data center 330 can be a physical datacenter. In other illustrative examples, data center 330 can be a virtualdata center located in a cloud computing environment such as cloudcomputing environment 50 in FIG. 1 and the different components for thiscloud computing environment in FIG. 2.

In this illustrative example, network manager 332 can operate to managea network such as data center 330. In this illustrative example, networkmanager 332 can run digital twins 334 as part of managing data center330. In this illustrative example, digital twins 334 are virtualrepresentations of physical objects or systems in data center 330.Digital twins 334 are in communication with each other. In this manner,and exchange of information between digital twins 334 can be used todetermine the impact of how the operation of one digital twin in digitaltwins 334 can impact other digital twins in digital twins 334. Inaddition, this communication also enables determining how the operationof the other digital twins can impact the digital twin in digital twins334.

In this illustrative example, the communications can be provided throughinterfaces such as application programming interfaces (APIs) located indigital twins 334. As depicted, digital twins 334 are virtualrepresentations of physical devices in which the software for digitaltwins 334 are in containers or microservices. In other illustrativeexamples, digital twins 334 can take other forms such as stand-aloneservers, virtual machines, or other suitable implementations. In thisdepicted example, digital twins 334 run on server computer 304. In otherillustrative examples, digital twins 334 can be run on other computerssuch as server computer 306, client computer 314, or some other suitabledata processing system. Network manager 332 can run on the same or on adifferent computer from digital twins 334.

Further, digital twins 334 can also receive real-time data 336 from datacenter 330. In this illustrative example, real-time data 336 can beoperational data relating to the operation of physical devices in datacenter 330. For example, real-time data 336 can include at least one oftemperature, airflow information, workload processing, memory usage,processor usage, network traffic, or other information describing oraffecting the operation of physical devices 412 in FIG. 4. Thisinformation can fluctuate based on the processing of workloads 414 inFIG. 4 by physical devices 412 as well as from other environmentalfactors. For example, scheduled maintenance, device failures, and otherfactors can affect real-time data 336.

Real-time data 336 can be obtained from sensors in data center 330. Thesensors can be physical sensors integrated as part of, connected to, orlocated proximate to physical devices represented by digital twins 334.Real-time data 336 can also be generated by physical devices in datacenter 330.

Real-time data 336 can be sent from data center 330 to network manager332 over network 102. In turn, network manager 332 can send or relayreal-time data 336 to digital twins 334. In other illustrative examples,real-time data 336 can be sent directly from data center 330 to digitaltwins 334.

In this illustrative example, real-time data 336 is data that is sent asquickly as possible without any intentional delay. Real-time data 336provides near real-time linkage between digital twins 334 and thephysical objects or systems and data center 330. The linkage provided byreal-time data 336 can provide real-time or near real-time simulation ofphysical objects or systems in data center 330 corresponding to digitaltwins 334.

In this illustrative example, digital twins 334 can be obtained fromdigital twin database 338. Digital twin database 338 contains digitaltwins that can be uploaded by vendors and manufacturers 339 of physicaldevices. Access to digital twin database 338 can be provided by a clientthrough a portal in network manager 332.

In the illustrative example, network manager 332 can analyze usagepatterns and incidents using real-time data 336. Further, networkmanager 332 can compare these usage patterns and incidents withhistorical information. Network manager 332 can also determine whetherdeviations from baselines for the operation of physical devicesrepresented by digital twins 334 have occurred. These types ofdeviations can result in network manager 332 performing a number ofdifferent actions. These actions can include generating an alert,scheduling maintenance, reallocating workloads, shutting down physicaldevices, or other suitable actions.

Additionally, network manager 232 can also respond to queries. Forexample, user 340 at client computer 314 can send query 342 to networkmanager 332. Query 342 can take a number of different forms. Forexample, query 342 can include a request for information about at leastone of a current of operation physical devices represented by digitaltwins 334, a hypothetical situation for data center 330, or otherinformation. The information can include insights and answers to query342 and be returned to user 340 at client computer 314 in response 346.With response 346, user 340 can make changes or adjustments to at leastone of the configuration, operation, maintenance, or other adjustmentsto data center 330.

Further, based on analysis of the operation of digital twins 334,network manager 332 can send feedback 344 to vendors and manufacturers339. Feedback 344 can be used to make updates or adjustments to at leastone of physical devices or digital twins 334 representing the physicaldevices supplied by vendors and manufacturers 339. Updates to digitaltwins 334 can then be uploaded to digital twin database 338 by vendorsand manufacturers 339.

In this manner, the use of digital twins 334 to represent physicaldevices and data center 330 can enable real-time or near real-timemanagement of data center 330. In the illustrative examples,communications between digital twins 334 are enabled such that theimpact of the operation of digital twins 334 on each other can be takeninto account. For example, the impact of heat output of a first physicaldevice represented in digital twins 334 can be determined with the firstphysical device that is near the air intake of a second physical devicerepresented in digital twins 334. In this manner, as the first physicaldevice performs more work, more heat is generated which directly impactsthe second device. In other words, workloads in particular physicaldevices can result in undesired heat being generated in differentlocations in data center 330. The use of digital twins 334 enablesdetermining when this type of undesired heat occurs or to predict whenthe undesired heat may occur in different locations in data center 330by running simulations of the workloads on digital twins 334.

In another illustrative example, the impact can be on power supplied todigital twins 334 in a simulation that represents the impact on thepower supplied to physical devices in data center 330 represented bydigital twins 334. Increased use of the power can result in increasingpower fluctuations depending on the load on a power supply. This type ofimpact on the digital twins can indicate power issues that may occurwith physical devices in data center 330 corresponding to digital twins334.

These types of impacts can be determined using digital twins 334 inthese illustrative examples. The ability to determine the impacts on theoperation of physical devices represented by digital twins 334 canenable taking actions that improve the performance, enable bettermanagement, and increase the life of the physical devices in data center330. As a result, at least one of outage avoidance, reduced maintenance,increased efficiency and power usage, or other desirable effects mayoccur when using digital twins 334 to model the operation of devices indata center 330.

With reference now to FIG. 4, a block diagram of a network managementenvironment is depicted in accordance with an illustrative embodiment.In this illustrative example, network management environment 400includes components that can be implemented in nodes 10 in cloudcomputing environment 50 in FIG. 1, physical devices in hardware andsoftware layer 60 in FIG. 2, and physical devices shown in network dataprocessing system 300 in FIG. 3.

In this illustrative example, network management system 402 operates tomanage network data processing system 404. Network data processingsystem 404 comprises computers and a network. The network is the mediumthat provides communications links between various devices and computersconnected together within network data processing system 404.

Network data processing system 404 can be used in a number of differentlocations. For example, network data processing system 404 can belocated in at least one of a data center, a manufacturing facility, adesign center, or some other location. The location of network dataprocessing system 404 can be a single area or distributed in differentareas. For example, when network data processing system 404 is a datacenter, the data center can be located in a single building or inmultiple buildings.

As depicted, network management system 402 comprises computer system 406and network manager 408. Network manager 408 is located in computersystem 406. Network manager 408 can be implemented in software,hardware, firmware, or a combination thereof. When software is used, theoperations performed by network manager 408 can be implemented inprogram code configured to run on hardware, such as a processor unit.When firmware is used, the operations performed by network manager 408can be implemented in program code and data and stored in persistentmemory to run on a processor unit. When hardware is employed, thehardware may include circuits that operate to perform the operations innetwork manager 408.

In the illustrative examples, the hardware may take a form selected fromat least one of a circuit system, an integrated circuit, an applicationspecific integrated circuit (ASIC), a programmable logic device, or someother suitable type of hardware configured to perform a number ofoperations. With a programmable logic device, the device can beconfigured to perform the number of operations. The device can bereconfigured at a later time or can be permanently configured to performthe number of operations. Programmable logic devices include, forexample, a programmable logic array, a programmable array logic, a fieldprogrammable logic array, a field programmable gate array, and othersuitable hardware devices. Additionally, the processes can beimplemented in organic components integrated with inorganic componentsand can be comprised entirely of organic components excluding a humanbeing. For example, the processes can be implemented as circuits inorganic semiconductors.

Computer system 406 is a physical hardware system and includes one ormore data processing systems. When more than one data processing systemis present in computer system 406, those data processing systems are incommunication with each other using a communications medium. Thecommunications medium can be a network. The data processing systems canbe selected from at least one of a computer, a server computer, a tabletcomputer, or some other suitable data processing system.

In this illustrative example, network manager 408 can run digital twins410 of physical devices 412 in network data processing system 404.Physical devices 412 can take a number of different forms. For example,physical devices 412 can be selected from at least one of a computer, aserver computer, a storage system, an uninterruptable power supply, apower distribution unit, a cooling device, a rack, a switch, a router, ahub, a bridge, a wireless access point, a display device, or some otherphysical device.

Digital twins 410 comprise software that is a virtual representation ofcorresponding physical devices. In other words, digital twins 410 canemulate physical devices 412. Digital twins 410 can be models and canprocess workloads 414. In the illustrative example, workloads 414 canrepresent the same workloads processed by physical devices 412. In otherillustrative examples, workloads 414 processed by digital twins 410 canbe hypothetical workloads selected for a simulation. In someillustrative examples, digital twins 410 can take the form of machinelearning models.

In the illustrative example, a digital twin in digital twins 410 canprocess workloads 414 in a number of different places. For example, thedigital twin can actually process a workload as part of the virtualrepresentation of the corresponding physical device. For example,processing a workload in workloads 414 can be performed using a “virtualworkload” for the digital twin representing the physical deviceprocessing the workload in workloads 414.

In another illustrative example, the digital twin can process theworkload in workloads 414 by processing the impact that the workload hason the physical device. For example, in processing the workload based onthe impact of the workload, the digital twin can receive informationsuch as sensor data and other application and workload metrics fromdifferent systems in network data processing system 404.

Additionally, digital twins 410 communicate with each other. In thisillustrative example, digital twins 410 correspond to physical devices412 in network data processing system 404. However, digital twins 410may not correspond to all of physical devices 412 in this illustrativeexample. For example, digital twins 410 corresponding to physicaldevices 412 can correspond to a portion of physical devices 412 that isless than all of physical devices 412 in network data processing system404. In other words, a digital twin may not be present for everyphysical device in network data processing system 404.

As depicted, a workload in workloads 414 can be the amount of work thata computer or set of computers is to perform. For example, the workloadcan be the total requests made by users and applications of a computeror a group of computers. The workload can also be measured with respectto the entire network data processing system. In the illustrativeexample, a workload running on a computer may not have a correspondingdigital twin for a physical device in physical devices 412 in networkdata processing system 404.

In this illustrative example, digital twins 410 can communicate witheach other using interfaces such as application programming interfaces(APIs). Digital twins 410 can communicate digital twin information 416to each other. In this manner, digital twins 410 can work together inproviding a virtual representation of physical devices 412 running innetwork data processing system 404.

By communicating digital twin information 416 to each other, a set ofdigital twins 410 can run in a manner that takes into account theoperation of another set of digital twins 410. In this illustrativeexample, digital twin information 416 from a digital twin is informationabout the digital twin and the operation of the digital twin. Further,digital twin information 416 can also include simulation results.

Network manager 408 can identify impact 418 on a number of parameters420 for first set 422 of digital twins 410 that second set 424 ofdigital twins 410 has on first set 422 of digital twins 410. In otherwords, the operation of one or more of digital twins 410 can have aneffect on one or more of digital twins 410.

Impact 418 is the effect or change that can be caused to the number ofparameters 420. In other words, impact 418 can cause values to changefor parameters 420. In this illustrative example, the number ofparameters 420 can be selected from at least one of a life, aperformance, a response time, a channel capacity, a latency, abandwidth, a processor resource use, a memory use, a power consumption,a temperature, an amount of heat generation, or some other parameter forfirst set 422 of digital twins 410.

For example, heat generation by a first digital twin can have an impacton the temperature of a second digital twin that is located adjacent tothe first digital twin. In other illustrative examples, anotherparameter in parameters 420 that can be impacted is airflow. In thisexample, the heat generation is a virtual representation of heat for thesimulation of heat generated by the corresponding physical device. Inother illustrative examples, the heat generation by the first digitaltwin can be based on receiving sensor data identifying the heatgenerated by the physical device corresponding to the digital twin. Inthis illustrative example, the heat generation can be detected in sensordata sent in real-time.

Further, other parameters in parameters 420 can include, for example, ashock or vibration in an area or in a device in the area that has animpact on devices in the area. For example, a physical impact on a rackcan have an impact on parameters for devices in the rack. As anotherexample, an impact on a power data unit (PDU) in the rack can have animpact on all devices connected to the power data unit. As anotherexample, humidity in the area may also have an impact on devices in thearea or devices surrounding the area.

As a result, knowing locations 426 of digital twins 410 can enabletaking into account the effect of a parameter such as heat generation bysecond set 424 of digital twins 410 on first set 422 of digital twins410. Locations 426 can also be locations for corresponding physicaldevices to digital twins 410. In other words, the determination of thelocation of a physical device and associating that location with thecorresponding digital twin can enable more accurate determinations ofimpact 418 on parameters 420.

As depicted, network manager 408 can perform a set of actions 428 basedon impact 418 on the number of parameters 420. In this illustrativeexample, the set of actions 428 can be selected from at least one ofscheduling maintenance, generating an alert, requesting a partreplacement, scheduling a workload, generating a ticket, moving theworkload from a first area that affects the set of parameters for anaffected physical device in an undesired manner to a second area thatdoes not affect the set of parameters for the affected physical devicein the undesired manner, or some other suitable action.

For example, scheduling operations in network data processing system 404can include setting a schedule for cooling devices such that cooling andpower usage can be optimized for network data processing system 404. Inthis illustrative example, “optimized” means that the cooling and powerusage can be improved, but is not necessarily the best cooling or powerusage. This type of scheduling can be used to increase the life ofvarious devices in network data processing system 404 by reducing ormaintaining desired levels of temperatures.

In the illustrative example, network manager 408 can determine impact418 on the number of parameters 420 based on processing of workloads 414by digital twins 410 and locations 426 of digital twins 410. Locations426 can be used to determine the impact of a parameter such astemperature.

In evaluating the current operation of network data processing system404, workloads 414 being processed by digital twins 410 correspond toworkloads 414 being processed by physical devices 412 corresponding todigital twins 410. Which workloads are being processed by which digitaltwins can be determined from network information 434.

In this illustrative example, at least one of identifying impact 418 orperforming the set of actions 428 can be performed by network manager408 using artificial intelligence system 430. As depicted, artificialintelligence system 430 is a system that has intelligent behavior andcan be based on the function of a human brain. An artificialintelligence system comprises at least one of an artificial neuralnetwork, a cognitive system, a Bayesian network, a fuzzy logic, anexpert system, a natural language system, or some other suitable system.Machine learning can be used to train artificial intelligence system430. Machine learning involves inputting data into the process andallowing the process to adjust and improve the function of artificialintelligence system 430.

In this illustrative example, artificial intelligence system 430includes a set of machine learning models 432. A machine learning modelis a type of artificial intelligence model that can learn without beingexplicitly programmed. A machine learning model can learn based ontraining data input into the machine learning model. The machinelearning model can learn using various types of machine learningalgorithms. The machine learning algorithms include at least one of asupervised learning, an unsupervised learning, a feature learning, asparse dictionary learning, an anomaly detection, association rules, orother types of learning algorithms. Examples of machine learning models432 include an artificial neural network, a decision tree, a supportvector machine, a Bayesian network, a genetic algorithm, and other typesof models. These machine learning models can be trained using data andprocess additional data to provide a desired output.

As a result, network manager 408 can operate to run simulations on adaily or ad hoc basis with digital twins 410 using at least one ofdigital twin information 416 or network information 434. The results ofthe simulations can be analyzed by network manager 408 to determineimpact 418 on a number of parameters 420 of one of more of digital twins410. This analysis can be used perform a set of actions 428. The set ofactions 428 can include sending information to manufacturers, vendors,or other parties. In this manner, updates to at least one of physicaldevices 412 for digital twins 410 can be performed.

Turning now to FIG. 5, a block diagram of a network manager is depictedin accordance with an illustrative embodiment. In the illustrativeexamples, the same reference numeral may be used in more than onefigure. This reuse of a reference numeral in different figuresrepresents the same element in the different figures.

In this illustrative example, network manager 408 can include a numberof different components. As depicted in this example, network manager408 Includes publish and subscribe system 500, deployment portal system502, impact analyzer 504, action initializer 506, learning system 508,query and reporting 510, device analyzer 512, and workload mover 513.

In this illustrative example, publish and subscribe system 500 enablesmanufacturers, vendors, or other parties to develop and store digitaltwins 410 in FIG. 4 in digital twin database 514. Publish and subscribesystem 500 enables publishing digital twins 410 stored in digital twindatabase 514 by users or other parties who have a subscription. Thesesubscriptions can be fee-based. As depicted, publish and subscribesystem 500 can enable a user to at least one of update digital twins 410or receive information about digital twins 410.

In this illustrative example, digital twins 410 represent physicaldevices 412 in FIG. 4. Digital twins 410 may be implemented in differentforms. For example, digital twins 410 can be containers or microservices. In this example, a container is a packaging mechanism fordeploying digital twins 410. For example, a container for a digitaltwin, such as a server computer, can include the application forprocesses emulating the server computer, application programminginterfaces (APIs), libraries, and other files or information.

Deployment portal system 502 is used to deploy digital twins 410 for usein creating a virtual representation of an actual network dataprocessing system. In this illustrative example, deployment portalsystem 502 comprises a set of deployment portals 516. A deploymentportal in the set of deployment portals 516 can be customized or set upspecifically for a particular user. Deployment portal system 502 can beset up in a hybrid multi-cloud setting such as in cloud computingenvironment 50 in FIG. 1.

In this illustrative example, a deployment portal can be employed by auser to download one or more of digital twins 410 for use in setting upa virtual representation of a virtual network data processing systemsuch as a data center. In one illustrative example, ratings, reviews,and other information about the performance of digital twins 410 can beobtained through the deployment portal. Further, deployment portalsystem 502 can be used to access interfaces such as applicationprogramming interfaces (APIs) to connect digital twins 410 to eachother.

Further, these interfaces can also be used to connect digital twins 410to other sources of information that may be needed for simulating theoperation of physical devices 412 in network data processing system 404in FIG. 4. For example, the other sources of information can be, networkinformation 434 in FIG. 4 from physical devices 412 in network dataprocessing system 404.

In this illustrative example, deployment portal system 502 can be usedto connect digital twins 410 to physical devices 412 to enablecommunications between digital twins 410 and physical devices 412 aswell as identify physical relationships between digital twins 410. Inthis illustrative example, the communications are one way in whichdigital twins 410 receive network information 434 in FIG. 4 from atleast one of physical devices 412 in FIG. 4 or other sources. In otherillustrative examples, communications can be bidirectional. The physicalrelationships can be, for example, a location of digital twins 410relative to each other based on locations 426 in FIG. 4 of physicaldevices 412 corresponding to digital twins 410.

In this manner, a user can select and configure digital twins 410 torepresent both the logical and physical relationships of correspondingphysical devices. With this type of setup, the impact of the operationof one digital twin on the operation of another digital twin can bedetermined in a manner that accurately represents the impact occurringon corresponding physical devices.

In this illustrative example, impact analyzer 504 can operate to analyzecurrent usage patterns 518 to determine impact 418 on a number ofparameters 420 in FIG. 4 for a first set of digital twins 410 that asecond set of digital twins 410 may have on the first set of digitaltwins 410. Impact analyzer 504 can compare current usage patterns 518 tohistorical usage patterns 520. This analysis can be used to determineimpact 418 on a number of parameters 420. This impact can be determinedfor one or more of digital twins 410.

In this illustrative example, usage patterns can include parameters 420in FIG. 4. With current usage patterns 518, parameters 420 are thecurrent or most recently obtained parameters. With historical usagepatterns 520, parameters 420 are parameters identified for prior periodsof time.

In this illustrative example, impact 418 can take the form of a scorefor each parameter or for the number of parameters 420 as a whole basedon the variation of current usage patterns 518 from historical usagepatterns 520. If the variation is great enough, the current usagepattern can be considered an abnormal usage pattern.

In this illustrative example, historical usage patterns 520 can be usedto determine baseline 522 for each physical device. Deviations incurrent usage patterns 518 from baseline 522 for a digital twin canindicate an action may be needed with respect to the physical devicecorresponding to the digital twin.

In other words, baseline 522 can be a learned insight generated by themachine learning model or other process using historical usage patterns520. In other illustrative examples, baseline 522 can be set by themanufacturer or vendor of the physical device.

In some illustrative examples, thresholds 524 may be identified fromhistorical usage patterns 520. These thresholds can be used in analyzingcurrent usage patterns 518 to determine whether actions are needed. Aset of thresholds 524 can be identified for each digital twin. Athreshold in the set of thresholds 524 can be identified automaticallyor set manually.

In this illustrative example, impact analyzer 504 can generate result526. Result 526 can include an indication of whether a threshold hasbeen exceeded. As another example, result 526 can include a scoreindicating the impact of each abnormal pattern identified in currentusage patterns 518. As another example, result 526 can also include theanalysis or comparison of current usage patterns 518 with historicalusage patterns 520.

In this illustrative example, action initializer 506 can perform a setof actions 428 based on result 526 generated by impact analyzer 504.Action initializer 506 can perform a set of actions 428 using result526. The set of actions 428 can take a number of different forms. Forexample, the set of actions 428 can be corrective actions or can be areporting of information or a status of network data processing system404 based on running digital twins 410. This information being reportedcan also include network information 434.

For example, the action can be based on an impact score or otherinformation in result 526. The set of actions 428 can be selected fromat least one of scheduling maintenance, generating an alert, requestinga part replacement, scheduling operations in network data processingsystem 404 in FIG. 4, scheduling a set of workloads 414, moving aworkload from a first area that affects a set of parameters for anaffected physical device in an undesired manner to a second area thatdoes not affect the set of parameters for the affected physical devicein the undesired manner, or some other suitable action. In thisillustrative example, scheduling the set of workloads 414 can involvescheduling at least one of a location or time for each workload in theset of workloads 414 to be performed.

As depicted, learning system 508 gathers information from theenvironment including digital twins 410. This gathered information canbe used to generate device information 528. In this illustrativeexample, device information 528 can include at least one of digital twininformation 416 or network information 434. Additionally, deviceinformation 528 can also include information derived from impact 418determined by impact analyzer 504.

Learning system 508 can be implemented using one or more machinelearning models. Learning system 508 can send this information topublishers or digital twins 410 or other parties.

For example, learning system 508 can send information about at least oneof digital twins 410 processing workloads 414 or impact 418 on thenumber of parameters 420 to manufacturers of physical devices 412 inFIG. 4 that digital twins 410 represent. In this manner, themanufacturers of digital twins 410 can make updates to at least one ofdigital twins 410 or physical devices 412. As another example, thisinformation can be used in designing new physical devices. Thisinformation can be sent using a push or pull mechanism.

Query and reporting 510 can be implemented in deployment portal system502. Query and reporting 510 can be used to obtain insights and answersto queries. For example, a user can use query and reporting 510 toobtain information about hypothetical situations that can includeperforming simulations for these hypothetical situations using digitaltwins 410. In the illustrative example, query and reporting 510 canreceive a query for at least one of a hypothetical situation orinformation about a set of physical devices 412 in FIG. 4 running on thenetwork data processing system 404 in FIG. 4.

Hypothetical situations can include, for example, determining impact 418on a particular uninterruptible power supply (UPS) if additional servercomputers are added to network data processing system 404 in FIG. 4.Further, impact 418 can be on a number of parameters 420 comprising alife. In this case, impact 418 can be how the life of theuninterruptible power supply and other devices connected to theuninterruptible power supply can be affected. Additional parameters thatcan be impacted can include, for example, maintenance, network traffic,airflow, or other parameters.

Query and reporting 510 can employ machine learning models andsimulation modules. In this manner, more information can be obtainedabout changes to situations that may result in refreshing or replacingdevices that may not be performing well, reaching end-of-life, orforming potential threats to network data processing system 404 in FIG.4.

In this illustrative example, device analyzer 512 can monitor impact 418on parameters 420, such as, life and performance of physical devices 412in FIG. 4 that are associated with digital twins 410. For example, thelife and performance can include measuring impact 418 of workloads 414being performed by physical devices 412. Workloads 414 being run onphysical devices 412 in a selected location can have an undesiredimpact. For example, temperatures can rise higher than desired in amanner that affects at least one of the performance of those andpotentially other physical devices. The performance can also include thelife of physical devices as well as resource usage. As another example,the temperatures may not be effective in an undesired manner. However,cooling devices in physical devices 412 can run in a manner that reducestheir life.

When device analyzer 512 determines that the change to performanceexceeds a threshold, workload mover 513 can move workloads 414 from anaffected location or device to another location or device within networkdata processing system 404 in FIG. 4 such that the impact to aparticular physical device is reduced. Further, device analyzer 512 cancontinue monitoring changes to performance to determine when workloads414 may be moved back to the location that was affected.

The different components in network manager 408 are shown as part of asingle block. In other illustrative examples, these different componentscan be distributed on different computers in different locations.Further, in some illustrative examples, network manager 408 may includeadditional components or omit some of the depicted components in thisexample. For example, network manager 408 may not include query andreporting 510 in some illustrative examples.

With reference to FIG. 6, an illustration of digital twin information isdepicted in accordance with an illustrative embodiment. In this figure,examples of types of information of digital twin information 416 inFIGS. 4-5 are illustrated.

As depicted, digital twin information 416 includes a number of differenttypes of data. In this example, digital twin information 416 comprisesprocessing data 600 and resource information 602.

For example, processing data 600 in digital twin information 416 caninclude at least one of processor use, workload execution, workloads ina queue for processing, workload scheduling information, processorloads, temperature, memory use, power use, or other suitable informationrelating to the operation of a digital twin processing workloads.

In this illustrative example, resource information 602 identifiesresources in a digital twin. Resource information 602 can include atleast one of processor type or types, memory size, memory type, powersupply unit characteristics, configuration information, or otherinformation about resources in a digital twin.

Digital twin information 416 can be received by digital twins 410 inFIG. 4 through interfaces including at least one of a portal applicationprogramming interfaces (APIs), or other interfaces for connectingdigital twins 410 to each other to enable digital twins 410 to receivedigital twin information 416. These interfaces enable an exchange ofdigital twin information 416 between digital twins 410 such that theimpact of the operation of one or more digital twins on another digitaltwin can be taken into account. For example, the exchange of digitaltwin information 416 enables determining the impact that digital twins410 have on each other when processing workloads 414 in FIG. 4-5.

Turning now to FIG. 7, an illustration of network information isdepicted in accordance with an illustrative embodiment. In this figure,examples of types of information in network information 434 areillustrated. As depicted, network information 434 includes a number ofdifferent types of data. In this example, network information 434comprises real-time data 700 and environmental information 702. Networkinformation 434 can be used by at least one of network manager 408 ordigital twins 410 to run digital twins 410 in FIG. 4.

In this illustrative example, real-time data 700 can be obtained fromsensors in network data processing system 404 in FIGS. 4-5. The sensorscan be attached to, located nearby, or integrated as part of physicaldevices 412 in FIG. 4 in network data processing system 404. Real-timedata 700 can be used to determine what and how physical devices 412 areoperating in network data processing system 404.

As depicted, real-time data 700 can be sent from sensors in a set ofphysical devices 412 to a set of digital twins 410 corresponding to theset of physical devices 412. In this illustrative example, real-timedata 700 is sent as quickly as possible without intentional delay fromthe sensors to at least one of network manager 408 or digital twins 410.For example, temperature sensors can be located near, on, or within aserver computer. These temperature sensors can send temperatureinformation in real-time data 700 about the server computer. As anotherexample, sensors can be used to detect airflow and temperature of theairflow within a data center, manufacturing floor, or other location ofnetwork data processing system 404 in FIG. 4. In yet another example,switches or routers can be used to detect traffic flowing throughnetwork data processing system 404.

Real-time data 700 can also be obtained directly from physical devices412 in FIG. 4. For example, a physical device can send information aboutworkloads and workload processing in real-time data 700. In otherillustrative examples, a physical device can buy information aboutprocessor speeds, memory use, diagnostic information, or otherinformation. In other illustrative examples, information about workloadscan be obtained from real-time data 700 obtained from other sources innetwork data processing system 404. For example, real-time data aboutworkloads can be obtained from a system management layer, a workmanagement system, a monitoring and performance management system, orsome other monitoring or performance logging tools that monitor physicaldevices in a manner that enables determining workloads being processedby those physical devices.

In this illustrative example, the use of real-time data 700 can be usedto increase the accuracy in determining impact 418 in FIGS. 4-5 whenrunning digital twins 410 in FIG. 4. As a result, a prediction ofpotential failures and asset criticality can be determined moreaccurately with this information.

Digital twins 410 of physical devices 412 in network data processingsystem 404 run using real-time data 700. For example, real-time data 700can include information about workloads 414 in FIGS. 4-5. For example,real-time data 700 may identify locations 426 in FIG. 4 of workloads 414being processed by digital twins 410. In this manner, a determinationcan be made as to whether digital twins 410 corresponding to physicaldevices 412 processing workloads 414 in locations 426 that are adjacentto each other or in a cluster such that a higher amount of heatgeneration occurs in contrast to digital twins 410 corresponding tophysical devices 412 processing workloads 414 in locations 426 that aredistributed or spread out from each other in network data processingsystem 404. In other words, as workloads 414 are more concentrated inlocations 426 that are closer to each other, the amount of heatgenerated in those locations increases. In this manner, network manager408 can determine where undesired concentrations of heat generation mayoccur from based on locations 426 of workloads 414.

As another example, real-time data 700 can include temperatures measuredby sensors in network data processing system 404. These temperatures canbe used by digital twins 410 to determine impact 418 on parameters 420.These temperatures can show how temperatures increase when workloads 414are being performed in locations 426 that are clustered together asopposed to being spread out within network data processing system 404.

With the use of real-time data 700, a near real-time comprehensivelinkage between physical and virtual devices can be enabled. This typeof linkage can increase the insights in determining impacts occurring onphysical devices 412. As a result, this information along with theinterconnection of digital twins 410 can help provide optimizedmanagement of physical devices 412. This management may be used toincrease the life of physical devices, reduce failures, avoid outages,schedule maintenance, or perform other actions that increase theperformance of a network data processing system.

In this illustrative example, environmental information 702 can be usedto determine information about the physical configuration of networkdata processing system 404 including configurations or locations 426 ofphysical devices 412. For example, locations 426 can include anidentification of the rack in which a server computer is located and theslot in which the server computer is positioned in the rack. As anotherexample, locations 426 can include information about the location of acooling device. Further, the orientation of the cooling device can alsobe included in locations 426. This orientation can indicate a directionof airflow generated by the cooling device. In this manner, digitaltwins 410 can determine the effect that physical devices 412 have oneach other when processing workloads 414.

In one illustrative example, environmental information 702 can includeinformation received from at least one of an enterprise data lake, amulti-cloud services platform, ticketing tools,system/server/network/power/rack management tools, service managementtools, orchestration and automation tools, authentication/authorizationtools, historical data, IoT sensors, IoT management systems, edgeservices, a digital floor map identifying the position of each physicaldevice including network connections, and environmental data identifyingwhere physical devices operate. This information enables integratingdigital twins 410 with entire information technology (IT) infrastructureof network data processing system 404 from end to end.

This environmental information can enable the interconnection of digitaltwins 410 within the specific environment for running simulations thatcan affect various physical devices within network data processingsystem 404 and determining impact 418 of connected physical devices.Further, this information enables digital twins 410 originating frommultiple vendors to run in a manner that simulates the operation ofcorresponding physical devices more accurately. Network information 434can be received by digital twins 410 through interfaces including atleast one of portal application programming interfaces (APIs), or otherinterfaces for connecting to various external systems to digital twins410 to enable digital twins 410 to receive information. In otherillustrative examples, network information 434 may include real-timedata 700 or environmental information 702, but not both types ofinformation.

In one illustrative example, one or more solutions are present thatovercome a problem with managing a network data processing system suchas a data center. As a result, one or more solutions may provide aneffect of enabling detecting situations in which actions should beperformed to maintain or increase performance of a network dataprocessing system. This performance can be at least one of increasedprocessing capability, reduced power usage, reduced heat concentrations,increased life cycle for physical devices, reduced maintenance, or othertypes of performance.

Thus, network management system 402 can enable interconnection betweendigital twins 410 such that digital twins 410 can communicate with eachother. This type of communication can enable determining when onedigital twin impacts another digital twin based on at least one ofworkloads 414 for locations 426 of digital twins 410. Locations 426 ofdigital twins 410 correspond to the physical locations of correspondingphysical devices in physical devices 412 to digital twins 410. In theillustrative examples, these locations can be a three-dimensional space.This type of location can take into account locations within a rack.

Further, locations 426 can also include the orientation of physicaldevices 412. For example, the orientation of a cooling unit can indicatethe direction of airflow for use in determining temperature and heateffects in network data processing system 404. This type of simulationand analysis is in contrast to current techniques which only look at asingle digital twin and do not take into account how the operation ofone digital twin can affect another digital twin.

In this manner, a set of actions 428, such as corrective actions, can betaken to reduce undesired parameters such as heat. For example,workloads can be reallocated in a manner that reduces the generation ofheat in a particular location.

Computer system 406 can be configured to perform at least one of thesteps, operations, or actions described in the different illustrativeexamples using software, hardware, firmware, or a combination thereof.As a result, computer system 406 operates as a special purpose computersystem in which network manager 408 in computer system 406 enablesmanaging a network data processing system using digital twins thatcommunicate with each other. In particular, network manager 408transforms computer system 406 into a special purpose computer system ascompared to currently available general computer systems that do nothave network manager 408.

In the illustrative example, the use of network manager 408 in computersystem 406 integrates processes into a practical application for amethod to manage network data processing system 404 that increases theperformance of network data processing system 404. Further, theperformance of computer system 406 increases because of an ability tomore accurately monitor the performance of network data processingsystem 404 using digital twins 410 that communicate with each other. Inother words, network manager 408 in computer system 406 is directed to apractical application of processes integrated into network manager 408in computer system 406 that runs digital twins 410 of physical devicesin network data processing system 404 in which digital twins 410 processworkloads and communicate with each other.

Additionally, impact 418 on a number of parameters 420 for first set 422of digital twins 410 that second set 424 of digital twins 410 has onfirst set 422 of digital twins 410 can be identified. One or moreactions 428 can be performed based on impact 418 on the number ofparameters 420. In this illustrative example, network manager 408 incomputer system 406 provides a practical application for managingnetwork data processing system 404 such that the functioning of at leastone of computer system 406 or network data processing system 404 isimproved. For example, the performance of computer system 406 can beimproved by enabling a more accurate impact of one digital twin onanother digital twin.

Further, the illustrative examples provide a practical application formanaging network data processing system 404 using digital twins 410 thatcommunicate with each other and can also receive information fromnetwork data processing system 404. In this case, this informationcommunicated between digital twins 410 can enable taking one or moreactions 428 in a manner that improves the performance of network dataprocessing system 404.

The illustration of network management environment 400 and the differentcomponents in FIGS. 4-7 is not meant to imply physical or architecturallimitations to the manner in which an illustrative embodiment can beimplemented. Other components in addition to or in place of the onesillustrated may be used. Some components may be unnecessary. Also, theblocks are presented to illustrate some functional components. One ormore of these blocks may be combined, divided, or combined and dividedinto different blocks when implemented in an illustrative embodiment.

For example, artificial intelligence system 430 with a set of machinelearning models 432 can be implemented as part of network manager 408 insome implementations. As another example, network manager 408 is shownas a single component. In some illustrative examples, functions fornetwork manager 408 can be separated into separate components that canbe run on the same or different computers in computer system 406.Further, multiple copies of network manager 408 can be present in whicheach network manager can manage a different network data processingsystem. In yet other illustrative examples, network manager 408 canmanage one or more network data processing systems in addition to or inplace of network data processing system 404.

In another illustrative example, digital twins 410 can also representworkloads 414 running on computers or other devices. In other words,digital twins 410 can also include a representation of a workload inaddition to physical devices 412. In other words, when a workload inworkloads 414 is running on multiple devices and physical devices 412, adigital twin in digital twins 410 can be present representing thatworkload.

Turning next to FIG. 8, a flowchart of a process for managing a networkdata processing system is depicted in accordance with an illustrativeembodiment. The process in FIG. 8 can be implemented in hardware,software, or both. When implemented in software, the process can takethe form of program code that is run by one or more processor unitslocated in one or more hardware devices in one or more computer systems.For example, the process can be implemented in network manager 408 incomputer system 406 in FIG. 4.

The process begins by running digital twins of physical devices in anetwork data processing system, wherein the digital twins processworkloads and the digital twins communicate with each other (step 800).The process identifies an impact on a number of parameters for a firstset of the digital twins that a second set of the digital twins has onthe first set of the digital twins (step 802).

The process performs a set of actions based on the impact on the numberof parameters (step 804). The process terminates thereafter.

With reference to FIG. 9, another flowchart of a process for managing anetwork data processing system is depicted in accordance with anillustrative embodiment. The process in FIG. 9 depicts an additionalstep that can be performed for the process illustrated in FIG. 8. Thisstep can be run concurrently with other steps affected in FIG. 8

The process sends real-time data from sensors in a set of physicaldevices to a set of digital twins corresponding to the set of physicaldevices (step 900). The process terminates thereafter. With this step,running of the digital twins in step 800 in FIG. 8 can be performed byrunning the digital twins of physical devices in the network dataprocessing system using the real-time data.

Turning now to FIG. 10, yet another flowchart of a process for managinga network data processing system is depicted in accordance with anillustrative embodiment. The process in FIG. 10 depicts an additionalstep that can be performed for the process illustrated in FIG. 8. Thisstep can be run concurrently with other steps affected in FIG. 8.

The process sends information about at least one of digital twinsprocessing workloads or an impact on a number of parameters tomanufacturers of physical devices that the digital twins represent (step1000). The process terminates thereafter.

In FIG. 11, a flowchart of a process identifying an impact on a numberof parameters is depicted in accordance with an illustrative embodiment.The process in FIG. 11 is an example of an implementation for step 802in FIG. 8.

The process determines an impact on a number of parameters based onprocessing of workloads by digital twins and locations of the digitaltwins (step 1100). The process terminates thereafter.

In step 1100, the locations of the digital twins processing theworkloads can impact parameters such as temperature, power use, load oncooling devices, and other parameters that may be affected by theworkloads being processed by the digital twins in close proximity toeach other. In other words, as the workloads are processed by thedigital twins that are closer to each other, the effect on these typesof parameters can increase.

The flowcharts and block diagrams in the different depicted embodimentsillustrate the architecture, functionality, and operation of somepossible implementations of apparatuses and methods in an illustrativeembodiment. In this regard, each block in the flowcharts or blockdiagrams may represent at least one of a module, a segment, a function,or a portion of an operation or step. For example, one or more of theblocks can be implemented as program code, hardware, or a combination ofthe program code and hardware. When implemented in hardware, thehardware may, for example, take the form of integrated circuits that aremanufactured or configured to perform one or more operations in theflowcharts or block diagrams. When implemented as a combination ofprogram code and hardware, the implementation may take the form offirmware. Each block in the flowcharts or the block diagrams can beimplemented using special purpose hardware systems that perform thedifferent operations or combinations of special purpose hardware andprogram code run by the special purpose hardware.

In some alternative implementations of an illustrative embodiment, thefunction or functions noted in the blocks may occur out of the ordernoted in the figures.

For example, in some cases, two blocks shown in succession can beperformed substantially concurrently, or the blocks may sometimes beperformed in the reverse order, depending upon the functionalityinvolved. Also, other blocks can be added in addition to the illustratedblocks in a flowchart or block diagram.

Turning now to FIG. 12, a block diagram of a data processing system isdepicted in accordance with an illustrative embodiment. Data processingsystem 1200 can be used to implement server computer 104, servercomputer 106, client devices 110, in FIG. 1. Data processing system 1200can also be used to implement nodes 10 in FIG. 1, personal digitalassistant (PDA) or cellular telephone 54A in FIG. 1, desktop computer54B in FIG. 1, laptop computer 54C in FIG. 1, automobile computer system54N in FIG. 1, hardware in hardware and software layer 60 in FIG. 2, andcomputer system 406 in FIG. 4. In this illustrative example, dataprocessing system 1200 includes communications framework 1202, whichprovides communications between processor unit 1204, memory 1206,persistent storage 1208, communications unit 1210, input/output (I/O)unit 1212, and display 1214. In this example, communications framework1202 takes the form of a bus system.

Processor unit 1204 serves to process instructions for software that canbe loaded into memory 1206. Processor unit 1204 includes one or moreprocessors. For example, processor unit 1204 can be selected from atleast one of a multicore processor, a central processing unit (CPU), agraphics processing unit (GPU), a physics processing unit (PPU), adigital signal processor (DSP), a network processor, or some othersuitable type of processor. Further, processor unit 1204 can may beimplemented using one or more heterogeneous processor systems in which amain processor is present with secondary processors on a single chip. Asanother illustrative example, processor unit 1204 can be a symmetricmulti-processor system containing multiple processors of the same typeon a single chip.

Memory 1206 and persistent storage 1208 are examples of storage devices1216. A storage device is any piece of hardware that is capable ofstoring information, such as, for example, without limitation, at leastone of data, program code in functional form, or other suitableinformation either on a temporary basis, a permanent basis, or both on atemporary basis and a permanent basis. Storage devices 1216 may also bereferred to as computer-readable storage devices in these illustrativeexamples. Memory 1206, in these examples, can be, for example, arandom-access memory or any other suitable volatile or non-volatilestorage device. Persistent storage 1208 may take various forms,depending on the particular implementation.

For example, persistent storage 1208 may contain one or more componentsor devices. For example, persistent storage 1208 can be a hard drive, asolid-state drive (SSD), a flash memory, a rewritable optical disk, arewritable magnetic tape, or some combination of the above. The mediaused by persistent storage 1208 also can be removable. For example, aremovable hard drive can be used for persistent storage 1208.

Communications unit 1210, in these illustrative examples, provides forcommunications with other data processing systems or devices. In theseillustrative examples, communications unit 1210 is a network interfacecard.

Input/output unit 1212 allows for input and output of data with otherdevices that can be connected to data processing system 1200. Forexample, input/output unit 1212 may provide a connection for user inputthrough at least one of a keyboard, a mouse, or some other suitableinput device. Further, input/output unit 1212 may send output to aprinter. Display 1214 provides a mechanism to display information to auser.

Instructions for at least one of the operating system, applications, orprograms can be located in storage devices 1216, which are incommunication with processor unit 1204 through communications framework1202. The processes of the different embodiments can be performed byprocessor unit 1204 using computer-implemented instructions, which maybe located in a memory, such as memory 1206.

These instructions are referred to as program code, computer usableprogram code, or computer-readable program code that can be read and runby a processor in processor unit 1204. The program code in the differentembodiments can be embodied on different physical or computer-readablestorage media, such as memory 1206 or persistent storage 1208.

Program code 1218 is located in a functional form on computer-readablemedia 1220 that is selectively removable and can be loaded onto ortransferred to data processing system 1200 for execution by processorunit 1204. Program code 1218 and computer-readable media 1220 formcomputer program product 1222 in these illustrative examples. In theillustrative example, computer-readable media 1220 is computer-readablestorage media 1224.

In these illustrative examples, computer-readable storage media 1224 isa physical or tangible storage device used to store program code 1218rather than a medium that propagates or transmits program code 1218.

Alternatively, program code 1218 can be transferred to data processingsystem 1200 using a computer-readable signal media. Thecomputer-readable signal media can be, for example, a propagated datasignal containing program code 1218. For example, the computer-readablesignal media can be at least one of an electromagnetic signal, anoptical signal, or any other suitable type of signal. These signals canbe transmitted over connections, such as wireless connections, opticalfiber cable, coaxial cable, a wire, or any other suitable type ofconnection.

Further, as used herein, “computer-readable media 1220” can be singularor plural. For example, program code 1218 can be located incomputer-readable media 1220 in the form of a single storage device orsystem. In another example, program code 1218 can be located incomputer-readable media 1220 that is distributed in multiple dataprocessing systems. In other words, some instructions in program code1218 can be located in one data processing system while otherinstructions in program code 1218 can be located in one data processingsystem. For example, a portion of program code 1218 can be located incomputer-readable media 1220 in a server computer while another portionof program code 1218 can be located in computer-readable media 1220located in a set of client computers.

The different components illustrated for data processing system 1200 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments can be implemented. In some illustrative examples,one or more of the components may be incorporated in or otherwise form aportion of, another component. For example, memory 1206, or portionsthereof, may be incorporated in processor unit 1204 in some illustrativeexamples. The different illustrative embodiments can be implemented in adata processing system including components in addition to or in placeof those illustrated for data processing system 1200. Other componentsshown in FIG. 12 can be varied from the illustrative examples shown. Thedifferent embodiments can be implemented using any hardware device orsystem capable of running program code 1218.

Thus, the illustrative embodiments provide a method, apparatus, system,and computer program product for managing a network. In one illustrativeexample, digital twins of physical devices in a network data processingsystem are run by a computer system. The digital twins process workloadsand the digital twins communicate with each other. An impact on a numberof parameters for a first set of the digital twins that a second set ofthe digital twins has on the first set of the digital twins isidentified by the computer system. A set of actions is performed by thecomputer system based on the impact on the number of parameters.

With one or more illustrative examples, the impact on a number ofparameters including environmental parameters can be determined based oncontinuous monitoring and providing the monitoring of those parametersas input to the digital twins. With the use of the digital twins, anidentification of undesired impacts affecting physical devicescorresponding to the digital twins can be identified. For example,undesired heat, pressure, vibrations, or other parameters within desiredlevels can be determined.

With the use of network information including at least one of real-timedata or environmental information, a near real-time comprehensivelinkage between physical and virtual devices can be enabled. This typeof linkage can increase insights in determining impacts occurring on thephysical devices. As a result, this information along with theinterconnection of the digital twins can help provide better managementof physical devices. This management may be used to increase the life ofthe physical devices, reduce failures, avoid outages, schedulemaintenance, or perform other actions that increase the performance of anetwork data processing system, such as a data center.

In the illustrative example, the impact on performance from performingsimulations and analyzing the results of the simulations using thedigital twins can be used to readjust workloads that are assigned todifferent physical devices. In these illustrative examples, the digitaltwins communicate with each other and can also receive networkinformation from the network data processing system. In this manner, theimpact of the digital twins on each other can be analyzed in determiningwhether an action should be performed with respect to the network dataprocessing system. With the use of a network management system with thedigital twins in communication with each other, better informationsharing can be enabled between different parties and downtime can bereduced.

The description of the different illustrative embodiments has beenpresented for purposes of illustration and description and is notintended to be exhaustive or limited to the embodiments in the formdisclosed. The different illustrative examples describe components thatperform actions or operations. In an illustrative embodiment, acomponent can be configured to perform the action or operationdescribed. For example, the component can have a configuration or designfor a structure that provides the component an ability to perform theaction or operation that is described in the illustrative examples asbeing performed by the component. Further, to the extent that terms“includes”, “including”, “has”, “contains”, and variants thereof areused herein, such terms are intended to be inclusive in a manner similarto the term “comprises” as an open transition word without precludingany additional or other elements.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Not allembodiments will include all of the features described in theillustrative examples. Further, different illustrative embodiments mayprovide different features as compared to other illustrativeembodiments. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiment. The terminology used herein was chosen tobest explain the principles of the embodiment, the practical applicationor technical improvement over technologies found in the marketplace, orto enable others of ordinary skill in the art to understand theembodiments disclosed here.

What is claimed is:
 1. A method for managing a network data processingsystem, comprising: sending real-time data from sensors in a firstsystem in the network data processing system and a second system in thenetwork data processing system to a plurality of digital twins, wherein:the first system comprises a first set of one or more physical devicesand the second system comprises a second set of one or more physicaldevices independent of the first set of physical devices; running, by acomputer system, the digital twins, the digital twins including at leasta first set of digital twins of the first system and a second set ofdigital twins of the second system, wherein: the digital twins processworkloads and the digital twins communicate with each other, andrunning, by the computer system, the digital twins of the physicaldevices in the network data processing system comprises running, by thecomputer system, the digital twins of the physical devices in thenetwork data processing system using the real-time data; identifying, bythe computer system, an impact on a number of parameters for the firstset of the digital twins directly caused by the second set of thedigital twins based on a comparison of baseline parameters of only thefirst set of digital twins with measured parameters of the first set ofdigital twins and the second set of digital twins, the impactrepresenting an impact occurring on the first system caused by thesecond system; and performing, by the computer system, a set of actionswith regard to the first system and/or the second system based on theimpact on the number of parameters.
 2. The method of claim 1 furthercomprising: sending information about at least one of the digital twinsprocessing the workloads or the impact on the number of parameters tomanufacturers of the physical devices that the digital twins represent.3. The method of claim 1, wherein identifying, by the computer system,the impact on the number of parameters that the first set of the digitaltwins has on the second set of the digital twins comprises: determiningthe impact on the number of parameters based on processing of theworkloads by the digital twins and locations of the digital twins. 4.The method of claim 1, wherein identifying, by the computer system, theimpact on the number of parameters that the first set of the digitaltwins has on the second set of the digital twins comprises: identifying,by the computer system, the impact on the number of parameters that thefirst set of the digital twins has on the second set of the digitaltwins using a set of machine learning models.
 5. The method of claim 1,wherein the impact on the number of parameters is selected from at leastone of a life, a performance, a response time, a channel capacity, alatency, a bandwidth, a processor resource use, a memory use, a powerconsumption, a temperature, or an amount of heat generation.
 6. Themethod of claim 1, wherein the set of actions is selected from at leastone of scheduling maintenance, generating an alert, requesting a partreplacement, scheduling operations in the network data processingsystem, scheduling a set of workloads, generating a ticket, or movingthe workload from a first area that affects the number of parameters foran affected physical device in an undesired manner to a second area thatdoes not affect the number of parameters for the affected physicaldevice in the undesired manner.
 7. A network management systemcomprising: a computer system; and a hardware manager in the computersystem, wherein the hardware manager sends real-time data from sensorsin a first system in the network management system and a second systemin the network management system to a plurality of digital twins,wherein the first system comprises a first set of one or more physicaldevices and the second system comprises a second set of one or morephysical devices independent of the first set of physical devices: runsthe digital twins, the digital twins including at least a first set ofdigital twins of the first system and a second set of digital twins ofthe second system, wherein the digital twins process workloads and thedigital twins communicate with each other; wherein runs the digitaltwins comprises runs the digital twins of the physical devices in thenetwork management system using the real-time data; identifies an impacton a number of parameters for the first set of the digital twinsdirectly caused by the second set of the digital twins based on acomparison of baseline parameters of only the first set of digital twinswith measured parameters of the first set of digital twins and thesecond set of digital twins, the impact representing an impact occurringon the first system caused by the second system; and performs a set ofactions with regard to the first system and/or the second system basedon the impact on the number of parameters.
 8. The network managementsystem of claim 7, wherein the hardware manager sends information aboutat least one of the digital twins processing the workloads or the impacton the number of parameters to manufacturers of the physical devicesthat the digital twins represent.
 9. The network management system ofclaim 7, wherein in identifying the impact on the number of parametersthat the first set of the digital twins has on the second set of thedigital twins, the hardware manager determines the impact on the numberof parameters based on processing of the workloads by the digital twinsand locations of the digital twins.
 10. The network management system ofclaim 7, wherein in identifying the impact on the number of parametersthat the first set of the digital twins has on the second set of thedigital twins, the hardware manager identifies the impact on the numberof parameters that the first set of the digital twins has on the secondset of the digital twins using a set of machine learning models.
 11. Thenetwork management system of claim 7 further comprising: a publish andsubscribe system that enables a user to at least one of update thedigital twins or receive information about the digital twins.
 12. Thenetwork management system of claim 7 further comprising: a deploymentportal system that provides an interface to connect the digital twins toeach other such that the digital twins communicate with each other andwith other physical devices in the network data processing system. 13.The network management system of claim 7, wherein the hardware managerreceives a query for at least one of a hypothetical situation orinformation about a set of physical devices running in the network dataprocessing system.
 14. The network management system of claim 7, whereinthe impact on the number of parameters is selected from at least one ofa life, a performance, a response time, a channel capacity, a latency, abandwidth, a processor resource use, a memory use, a power consumption,a temperature, or a heat generation.
 15. The network management systemof claim 7, wherein the set of actions is selected from at least one ofscheduling maintenance, generating an alert, requesting a partreplacement, scheduling operations in the network data processingsystem, scheduling the workloads, generating a ticket, or a moving aworkload from a first area that affects the number of parameters for anaffected physical device in an undesired manner to a second area thatdoes not affect the number of parameters for the affected physicaldevice in the undesired manner.
 16. The network management system ofclaim 7, wherein the network data processing system is located in one ofa data center, a manufacturing facility, and a design center.
 17. Thenetwork management system of claim 7, wherein the physical devices areselected from at least one of a computer, a server computer, a storagesystem, an uninterruptable power supply, a power distribution unit, acooling device, a rack, a switch, a router, a hub, a bridge, a wirelessaccess point, or a display device.
 18. A computer program product formanaging a network data processing system, the computer program productcomprising: a computer-readable storage media; first program code,stored on the computer-readable storage media, executable by a computersystem to cause the computer system to send real-time data from sensorsin a first system in the network data processing system and a secondsystem in the network data processing system to a plurality of digitaltwins, wherein the first system comprises a first set of one or morephysical devices and the second system comprises a second set of one ormore physical devices independent of the first set of physical devices;second program code, stored on the computer-readable storage media,executable by the computer system to cause the computer system to runthe digital twins, the digital twins including at least a first set ofdigital twins of the first system and a second set of digital twins ofthe second system, wherein the digital twins process workloads and thedigital twins communicate with each other, and wherein run the digitaltwins comprises running the digital twins of the physical devices in thenetwork data processing system using the real-time data; third programcode, stored on the computer-readable storage media, executable by thecomputer system to cause the computer system to identify an impact on anumber of parameters for the first set of the digital twins directlycaused by the second set of the digital twins based on a comparison ofbaseline parameters of only the first set of digital twins with measuredparameters of the first set of digital twins and the second set ofdigital twins, the impact representing an impact occurring on the firstsystem and caused by the second system; and fourth program code, storedon the computer-readable storage media, executable by the computersystem to cause the computer system to perform a set of actions withregard to the first system and/or the second system based on the impacton the number of parameters.